Estimation of final product concentration in metalic ores using convolutional neural networks
Authors:
- Jakub Progorowicz,
- Artur Skoczylas,
- Sergii Anufriiev,
- Marek Dudzik,
- Paweł Stefaniak
Abstract
Although artificial neural networks are widely used in various fields, including mining and mineral processing, they can be problematic for appropriately choosing the model architecture and parameters. In this article, we describe a procedure for the optimization of the structure of a convolutional neural network model developed for the purposes of metallic ore pre-concentration. The developed model takes as an input two-band X-ray scans of ore grains, and for each scan two values corresponding to concentrations of zinc and lead are returned by the model. The whole process of sample preparation and data augmentation, optimization of the model hyperparameters and training of selected models is described. The ten best models were trained ten times each in order to select the best possible one. We were able to achieve a Pearson coefficient of R = 0.944 for the best model. The detailed results of this model are shown, and finally, its applicability and limitations in real-world scenarios are discussed.
- Record ID
- CUT9d3d1b93244c494c936104b7f89103c4
- Publication categories
- ;
- Author
- Journal series
- Minerals, ISSN , e-ISSN 2075-163X, Monthly
- Issue year
- 2022
- Vol
- 12
- No
- 12
- Pages
- [1-13]
- Article number
- 1480
- Other elements of collation
- fot.; schem.; tab.; wykr.; Bibliografia (na s.) - 12-13; Bibliografia (liczba pozycji) - 37; Oznaczenie streszczenia - Abstr.; Numeracja w czasopiśmie - Vol. 12, Iss. 12
- Substantive notes
- Special Issue: Innovative Solutions for Measurements, Modelling and Control in Mineral Processing
- Keywords in English
- convolutional neural networks, mineral processing, artificial intelligence, sensor-based sorting, ore enrichment, ore pre-concentration
- ASJC Classification
- ;
- DOI
- DOI:10.3390/min12121480 Opening in a new tab
- URL
- https://www.mdpi.com/2075-163X/12/12/1480 Opening in a new tab
- Related project
- Opracowanie innowacyjnej technologii wzbogacania rud metali nieżelaznych z wykorzystaniem systemu prekoncentracji opartego o algorytmy sztucznej inteligencji. . Project leader at PK: , ,
- Language
- eng (en) English
- License
- Score (nominal)
- 100
- Score source
- journalList
- Score
- Publication indicators
- Uniform Resource Identifier
- https://cris.pk.edu.pl/info/article/CUT9d3d1b93244c494c936104b7f89103c4/
- URN
urn:pkr-prod:CUT9d3d1b93244c494c936104b7f89103c4
* presented citation count is obtained through Internet information analysis, and it is close to the number calculated by the Publish or PerishOpening in a new tab system.